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Whitepapers Datanami's white paper database contains reports from the leading thought-leaders and idea generators in the Datanami industry.

Guide to Maximum Data Lake Value

Source: Qlik
Release Date: Sep 28, 2020

In this whitepaper, Eckerson Group discusses how to get maximum value from data lakes and how Qlik’s Data Integration Platform helps businesses get the most value out of their data lakes quickly, accurately, and with the agility to respond to shifting business needs. Read more…

The Essential Guide to Feature Selection

Source: Explorium
Release Date: Sep 4, 2020

Feature selection is a key step in building powerful and interpretable machine learning models, but it’s also one of the easiest to get wrong. The wrong features will give you inaccurate answers and may impact your ML models’ efficiency in ways you can’t predict. Read more…

How to Improve Your Training Data for Vastly Better Machine Learning

Source: Explorium
Release Date: Aug 13, 2020

Your machine learning models are only as good as the data you’re using to train and test them. So, how can you improve your datasets? This guide breaks down simple strategies to acquire better data and quick approaches and methods to fine-tune and manipulate your existing data will get you better testing results and insights. Read more…

8 Key Considerations for AI in the Enterprise

Source: H2O
Release Date: Jul 9, 2020

If you’re developing or thinking of developing an AI strategy to transform your business, there’s a lot to consider, let us help. We’re the creator of the leading open source machine learning and artificial intelligence platform and our vision is to democratize AI for all and empower every company to be an AI company. Read more…

AI is Making BI Obsolete, and Machine Learning is Leading the Way

Source: Explorium
Release Date: Jul 2, 2020

BI has become a must-get for any company, and while it does offer some great value, what are you really getting from it? Although BI is great at visualizing your data and giving you digestible reports, it’s hard to make predictions and automate your insights to really optimize your operations. Read more…

Making the Most of Your Investment in Hadoop

Source: Sqream
Release Date: Jun 5, 2020

Hadoop is a popular enabler for big data. But with data volumes growing exponentially, analytics have become restricted and painfully slow, requiring arduous data preparation. Often, querying weeks, months, or years of data is simply infeasible, and organizations succeed in analyzing only a fraction of their data. Read more…

Top Cloud Data Warehouses for the Enterprise

Source: Qlik
Release Date: May 29, 2020

Modern cloud architectures combine three essentials: the power of data warehousing, flexibility of Big Data platforms, and elasticity of cloud at a fraction of the cost to traditional solution users. Read more…

Big Data & Analytics Maturity Survey Report 2020

Source: AtScale
Release Date: May 18, 2020

Learn where Data, Analytics, and IT leaders are with their 2020 enterprise cloud strategy, their data and analytics priorities, and what their biggest challenges are. You’ll get key insights on their priorities around data virtualization, data governance, and data security across their on-premises and multi-cloud environments. Read more…

10 Rules for Managing Kafka

Source: Instaclustr
Release Date: May 15, 2020

Kafka is not difficult to use, but it is tricky to optimize. It has evolved from essentially a message queue to a versatile streaming platform. Read the 10 golden rules of managing Kafka that will help you perfect your Kafka system and stay ahead of the curve. Read more…

Six Success Factors for Getting Started with Machine Learning Across Your Enterprise

Source: H2O
Release Date: May 7, 2020

There is a wide range of ML use cases that can help organizations grow. However, the technology is still primarily in the early mainstream adoption stage. At TDWI, they see that many organizations get stuck when they start to make use of the technology, which can prevent it from being used in other parts of (or even across) the organization. Read more…